Segmentation of 3D radio frequency echocardiography using a spatio-temporal predictor

This paper presents an algorithm for segmenting left ventricular endocardial boundaries from RF ultrasound. Our method incorporates a computationally efficient linear predictor that exploits short-term spatio-temporal coherence in the RF data. Segmentation is achieved jointly using an independent identically distributed (i.i.d.) spatial model for RF intensity and a multiframe conditional model that relates neighboring frames in the image sequence. Segmentation using the RF data overcomes challenges due to image inhomogeneities often amplified in B-mode segmentation and provides geometric constraints for RF phase-based speckle tracking. The incorporation of multiple frames in the conditional model significantly increases the robustness and accuracy of the algorithm. Results are generated using between 2 and 5 frames of RF data for each segmentation and are validated by comparison with manual tracings and automated B-mode boundary detection using standard (Chan and Vese-based) level sets on echocardiographic images from 27 3D sequences acquired from six canine studies.

Figure optionsDownload high-quality image (120 K)Download as PowerPoint slideHighlights
► We segment left ventricular endocardial boundaries from RF ultrasound.
► Our M.A.P. segmentation uses a joint spatial model and a multiframe conditional.
► The conditional model relates neighboring frames using a linear predictor.
► The linear predictor exploits spatio-temporal coherence in the data.
► We overcome problems due to image inhomogeneities amplified in B-mode segmentation.